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DuckDuckGoSearch

This notebook provides a quick overview for getting started with DuckDuckGoSearch. For detailed documentation of all DuckDuckGoSearch features and configurations head to the API reference.

DuckDuckGoSearch offers a privacy-focused search API designed for LLM Agents. It provides seamless integration with a wide range of data sources, prioritizing user privacy and relevant search results.

Overview​

Integration details​

ClassPackagePY supportPackage latest
DuckDuckGoSearch@langchain/community✅NPM - Version

Setup​

The integration lives in the @langchain/community package, along with the duck-duck-scrape dependency:

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/community duck-duck-scrape

Credentials​

It’s also helpful (but not needed) to set up LangSmith for best-in-class observability:

process.env.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_API_KEY = "your-api-key";

Instantiation​

You can instantiate an instance of the DuckDuckGoSearch tool like this:

import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";

const tool = new DuckDuckGoSearch({ maxResults: 1 });

Invocation​

Invoke directly with args​

await tool.invoke("what is the current weather in sf?");
[{"title":"San Francisco, CA Current Weather | AccuWeather","link":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","snippet":"<b>Current</b> <b>weather</b> <b>in</b> San Francisco, CA. Check <b>current</b> conditions in San Francisco, CA with radar, hourly, and more."}]

Invoke with ToolCall​

We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:

// This is usually generated by a model, but we'll create a tool call directly for demo purposes.
const modelGeneratedToolCall = {
  args: {
    input: "what is the current weather in sf?",
  },
  id: "tool_call_id",
  name: tool.name,
  type: "tool_call",
};
await tool.invoke(modelGeneratedToolCall);
ToolMessage {
  "content": "[{\"title\":\"San Francisco, CA Weather Conditions | Weather Underground\",\"link\":\"https://www.wunderground.com/weather/us/ca/san-francisco\",\"snippet\":\"San Francisco <b>Weather</b> Forecasts. <b>Weather</b> Underground provides local & long-range <b>weather</b> forecasts, weatherreports, maps & tropical <b>weather</b> conditions for the San Francisco area.\"}]",
  "name": "duckduckgo-search",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "tool_call_id"
}

Chaining​

We can use our tool in a chain by first binding it to a tool-calling model and then calling it:

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/openai

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  temperature: 0
});
import { HumanMessage } from "@langchain/core/messages";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableLambda } from "@langchain/core/runnables";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant."],
  ["placeholder", "{messages}"],
]);

const llmWithTools = llm.bindTools([tool]);

const chain = prompt.pipe(llmWithTools);

const toolChain = RunnableLambda.from(async (userInput: string, config) => {
  const humanMessage = new HumanMessage(userInput);
  const aiMsg = await chain.invoke(
    {
      messages: [new HumanMessage(userInput)],
    },
    config
  );
  const toolMsgs = await tool.batch(aiMsg.tool_calls, config);
  return chain.invoke(
    {
      messages: [humanMessage, aiMsg, ...toolMsgs],
    },
    config
  );
});

const toolChainResult = await toolChain.invoke(
  "how many people have climbed mount everest?"
);
const { tool_calls, content } = toolChainResult;

console.log(
  "AIMessage",
  JSON.stringify(
    {
      tool_calls,
      content,
    },
    null,
    2
  )
);
AIMessage {
  "tool_calls": [],
  "content": "As of December 2023, a total of 6,664 different people have reached the summit of Mount Everest."
}

Agents​

For guides on how to use LangChain tools in agents, see the LangGraph.js docs.

API reference​

For detailed documentation of all DuckDuckGoSearch features and configurations head to the API reference

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